The exponential increase in IoT networks and devices raises a lot of challenges in achieving system dependability and efficiency especially in fault diagnosis and anomaly detection. The current research work proposes a statistical modeling framework that aims to tackle these challenges using time series analysis and regression models as well as Mahalanobis distance for real-time anomaly identification. The idea behind the proposed method is to identify aberrations in normal system functions, making it possible to offer a scalable solution for an IoT ecosystem. The comparison with results of the traditional anomaly detection shows that the presented approach provides better accuracy by 15% and recall by 20%. Also, the method is able to detect anomalies within a latency of under 2 seconds which is suitable in applications with strict response time. The flexibility of the proposed approach can be seen from the easy implementation in large data sets and flexibility to real networks’ conditions. The implication drawn from the findings indicates that statistical modeling makes IoT networks more reliable and secure.